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<p>As a green transport alternative, electric bikes (e-bikes) have experienced unprecedented global growth. Understanding their relationship with the built environment—especially path-related features, which critically influence unsheltered mode choice—is essential given their expanding fleets. Yet, few studies have quantitatively examined the role of path-based built environment on e-bike travel behavior. Using travel survey data from the Jinan metropolitan area in China, this study applied the XGBoost model to explore nonlinear associations between e-bike use and built environment features at the origin, destination, and along the path. Results indicate that: (1) e-bikes are primarily used for work commutes, followed by leisure and school trips, with average distances of 3.76&#xa0;km, 2.73&#xa0;km, and 2.20&#xa0;km, respectively; (2) built environment factors outweigh socioeconomic and travel attributes in e-bike choice, with path-level features being most influential; (3) travel distance is the dominant factor, showing an inverted-V relationship with a threshold around 3.5–4&#xa0;km; (4) route congestion exhibits an M-shaped association, while intersection density negatively correlates with e-bike use, and other route features (e.g., Green View Index) display inverted-V patterns. These findings support planning strategies for promoting e-bike use through cycling-friendly environments.</p>

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Non-Linear Effects of Built Environment on E-Bike Use by Travel Purposes: A Case Study of Jinan, China

  • 期刊论文

  • 发表日期：

    2026年5月20日

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  • Yuanyuan Guo,
  • Shutong Li,
  • Yixuan Wang,
  • Jiewen Liu,
  • Weijie Liang,
  • Jianjun Li,
  • Shuli Zhou

摘要

As a green transport alternative, electric bikes (e-bikes) have experienced unprecedented global growth. Understanding their relationship with the built environment—especially path-related features, which critically influence unsheltered mode choice—is essential given their expanding fleets. Yet, few studies have quantitatively examined the role of path-based built environment on e-bike travel behavior. Using travel survey data from the Jinan metropolitan area in China, this study applied the XGBoost model to explore nonlinear associations between e-bike use and built environment features at the origin, destination, and along the path. Results indicate that: (1) e-bikes are primarily used for work commutes, followed by leisure and school trips, with average distances of 3.76 km, 2.73 km, and 2.20 km, respectively; (2) built environment factors outweigh socioeconomic and travel attributes in e-bike choice, with path-level features being most influential; (3) travel distance is the dominant factor, showing an inverted-V relationship with a threshold around 3.5–4 km; (4) route congestion exhibits an M-shaped association, while intersection density negatively correlates with e-bike use, and other route features (e.g., Green View Index) display inverted-V patterns. These findings support planning strategies for promoting e-bike use through cycling-friendly environments.